Dev gzf (#1405)

zhifu gao

2024-02-29 c456abaf33023038be686f18df6a1178367d3894

Dev gzf (#1405)

* init param

 funasr/metrics/compute_wer.py

File was deleted

 funasr/metrics/wer.py

New file
@@ -0,0 +1,190 @@
import os
import numpy as np
import sys
import hydra
from omegaconf import DictConfig, OmegaConf, ListConfig


def compute_wer(ref_file,
                hyp_file,
                cer_file,
                cn_postprocess=False,
                ):
    rst = {
        'Wrd': 0,
        'Corr': 0,
        'Ins': 0,
        'Del': 0,
        'Sub': 0,
        'Snt': 0,
        'Err': 0.0,
        'S.Err': 0.0,
        'wrong_words': 0,
        'wrong_sentences': 0
    }
	
    hyp_dict = {}
    ref_dict = {}
    with open(hyp_file, 'r') as hyp_reader:
        for line in hyp_reader:
            key = line.strip().split()[0]
            value = line.strip().split()[1:]
            if cn_postprocess:
                value = " ".join(value)
                value = value.replace(" ", "")
                if value[0] == "请":
                    value = value[1:]
                value = [x for x in value]
            hyp_dict[key] = value
    with open(ref_file, 'r') as ref_reader:
        for line in ref_reader:
            key = line.strip().split()[0]
            value = line.strip().split()[1:]
            if cn_postprocess:
                value = " ".join(value)
                value = value.replace(" ", "")
                value = [x for x in value]
            ref_dict[key] = value
	
    cer_detail_writer = open(cer_file, 'w')
    for hyp_key in hyp_dict:
        if hyp_key in ref_dict:
            out_item = compute_wer_by_line(hyp_dict[hyp_key], ref_dict[hyp_key])
            rst['Wrd'] += out_item['nwords']
            rst['Corr'] += out_item['cor']
            rst['wrong_words'] += out_item['wrong']
            rst['Ins'] += out_item['ins']
            rst['Del'] += out_item['del']
            rst['Sub'] += out_item['sub']
            rst['Snt'] += 1
            if out_item['wrong'] > 0:
                rst['wrong_sentences'] += 1
            cer_detail_writer.write(hyp_key + print_cer_detail(out_item) + '\n')
            cer_detail_writer.write("ref:" + '\t' + " ".join(list(map(lambda x: x.lower(), ref_dict[hyp_key]))) + '\n')
            cer_detail_writer.write("hyp:" + '\t' + " ".join(list(map(lambda x: x.lower(), hyp_dict[hyp_key]))) + '\n')
            cer_detail_writer.flush()
	
    if rst['Wrd'] > 0:
        rst['Err'] = round(rst['wrong_words'] * 100 / rst['Wrd'], 2)
    if rst['Snt'] > 0:
        rst['S.Err'] = round(rst['wrong_sentences'] * 100 / rst['Snt'], 2)
	
    cer_detail_writer.write('\n')
    cer_detail_writer.write("%WER " + str(rst['Err']) + " [ " + str(rst['wrong_words']) + " / " + str(rst['Wrd']) +
                            ", " + str(rst['Ins']) + " ins, " + str(rst['Del']) + " del, " + str(
        rst['Sub']) + " sub ]" + '\n')
    cer_detail_writer.write(
        "%SER " + str(rst['S.Err']) + " [ " + str(rst['wrong_sentences']) + " / " + str(rst['Snt']) + " ]" + '\n')
    cer_detail_writer.write("Scored " + str(len(hyp_dict)) + " sentences, " + str(
        len(hyp_dict) - rst['Snt']) + " not present in hyp." + '\n')
	
    cer_detail_writer.close()


def compute_wer_by_line(hyp,
                        ref):
    hyp = list(map(lambda x: x.lower(), hyp))
    ref = list(map(lambda x: x.lower(), ref))
	
    len_hyp = len(hyp)
    len_ref = len(ref)
	
    cost_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int16)
	
    ops_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int8)
	
    for i in range(len_hyp + 1):
        cost_matrix[i][0] = i
    for j in range(len_ref + 1):
        cost_matrix[0][j] = j
	
    for i in range(1, len_hyp + 1):
        for j in range(1, len_ref + 1):
            if hyp[i - 1] == ref[j - 1]:
                cost_matrix[i][j] = cost_matrix[i - 1][j - 1]
            else:
                substitution = cost_matrix[i - 1][j - 1] + 1
                insertion = cost_matrix[i - 1][j] + 1
                deletion = cost_matrix[i][j - 1] + 1
				
                compare_val = [substitution, insertion, deletion]
				
                min_val = min(compare_val)
                operation_idx = compare_val.index(min_val) + 1
                cost_matrix[i][j] = min_val
                ops_matrix[i][j] = operation_idx
	
    match_idx = []
    i = len_hyp
    j = len_ref
    rst = {
        'nwords': len_ref,
        'cor': 0,
        'wrong': 0,
        'ins': 0,
        'del': 0,
        'sub': 0
    }
    while i >= 0 or j >= 0:
        i_idx = max(0, i)
        j_idx = max(0, j)
		
        if ops_matrix[i_idx][j_idx] == 0:  # correct
            if i - 1 >= 0 and j - 1 >= 0:
                match_idx.append((j - 1, i - 1))
                rst['cor'] += 1
			
            i -= 1
            j -= 1
		
        elif ops_matrix[i_idx][j_idx] == 2:  # insert
            i -= 1
            rst['ins'] += 1
		
        elif ops_matrix[i_idx][j_idx] == 3:  # delete
            j -= 1
            rst['del'] += 1
		
        elif ops_matrix[i_idx][j_idx] == 1:  # substitute
            i -= 1
            j -= 1
            rst['sub'] += 1
		
        if i < 0 and j >= 0:
            rst['del'] += 1
        elif j < 0 and i >= 0:
            rst['ins'] += 1
	
    match_idx.reverse()
    wrong_cnt = cost_matrix[len_hyp][len_ref]
    rst['wrong'] = wrong_cnt
	
    return rst


def print_cer_detail(rst):
    return ("(" + "nwords=" + str(rst['nwords']) + ",cor=" + str(rst['cor'])
            + ",ins=" + str(rst['ins']) + ",del=" + str(rst['del']) + ",sub="
            + str(rst['sub']) + ") corr:" + '{:.2%}'.format(rst['cor'] / rst['nwords'])
            + ",cer:" + '{:.2%}'.format(rst['wrong'] / rst['nwords']))


@hydra.main(config_name=None, version_base=None)
def main_hydra(cfg: DictConfig):
    ref_file = cfg.get("ref_file", None)
    hyp_file = cfg.get("hyp_file", None)
    cer_file = cfg.get("cer_file", None)
    cn_postprocess = cfg.get("cn_postprocess", False)
    if ref_file is None or hyp_file is None or cer_file is None:
        print(
            "usage : python -m  funasr.metrics.wer ++ref_file=test.ref ++hyp_file=test.hyp ++cer_file=test.wer ++cn_postprocess=false")
        sys.exit(0)
	
    compute_wer(ref_file, hyp_file, cer_file, cn_postprocess)


if __name__ == '__main__':
    main_hydra()




 funasr/models/llm_asr_nar/model.py

@@ -315,7 +315,10 @@
        model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=None)
        preds = torch.argmax(model_outputs.logits, -1)
        text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
        text = text[0].split(': \n')[-1]

        text = text[0].split(': ')[-1]
        text = text.strip()
        
        # preds = torch.argmax(model_outputs.logits, -1)
        
        ibest_writer = None